Advanced search

Advanced search is divided into two main parts, and one or more groups in each of the main parts. The main parts are the "Search for" (including) and the "Remove from search" (excluding) part. (The excluding part might not be visible until you hit "NOT" for the first time.) You can add new groups to both the including and the excluding part by using the buttons "OR" or "NOT" respectively, and you can add more search options to all groups through the drop down menu on the last row (in each group).

For a result to be included in the search result, is it required to fit all added including parameters (in at least one group) and not fit all parameters in one of the excluding groups. This system with the two main parts and their groups makes it possible to combine two (or more) distinct searches into one search result, while being flexible in removing results from the final list.

Limit the search further

Here you can limit your search further, your result list will only contain those who match all of the criteria that you fill out in this part (combined with the advanced search from above)

Abstract [en]

TV-on-Demand services have become one of the most popular Internet applications that continuously attracts high user interest. With rapidly increasing user demands, the existing network conditions may not be able to ensure a low start-up delay of video playback. Prefetching has been broadly investigated to cope with the start-up latency problem, which is also known as user perceived latency. In this paper, two datasets from different IPTV providers are used to analyse the TV program request patterns. According to the results, we propose a prefetching scheme at the user end to preload videos before user requests. For both datasets, our prefetching scheme significantly improves the cache hit ratio compared to passive caching and we note that there is a potential to further improve prefetching performance by customizing prefetching schemes for different video categories. We further present a cost model to determine the optimal number of videos to prefetch. We also discuss if there is enough time for prefetching. Finally, more factors, which may have an impact onoptimizing prefetching performance, are further discussed, such as the jump patterns over different time in a day and the the distribution of each video’s viewing length.